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Relevant bibliographies by topics / Deep Learning in Healthcare

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Academic literature on the topic 'Deep Learning in Healthcare'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Deep Learning in Healthcare"

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Hu, Shengkun. "Deep Learning in Healthcare." Highlights in Science, Engineering and Technology 57 (July 11, 2023): 279–85. http://dx.doi.org/10.54097/hset.v57i.10014.

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This article aims to discuss and demonstrate deep learning techniques used in healthcare. After introducing the feasibility of deep learning in the medical field, the article discussed the opportunities and challenges of deep learning in healthcare from different perspectives. Then, the article showed the current implementations and applications of deep learning in the medical healthcare system. Finally, the article summarizes deep learning techniques and applications in healthcare.

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B, Thulasi Thanmai, Vani K., Dwaraka Srihith I., Venkat Sai I., and Shasikala I. "Revolutionizing Healthcare with Deep Learning." Recent Trends in Information Technology and its Application 6, no. 3 (2023): 16–30. https://doi.org/10.5281/zenodo.8138446.

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<em>Deep learning technology is becoming increasingly prevalent in the healthcare industry, which has the potential to revolutionise medical diagnosis, treatment, and patient care. Deep learning algorithms are capable of analysing immense quantities of healthcare data, such as patient records, medical images, and genomic information, to identify patterns and make highly accurate predictions. This technology is currently being used, among other things, to enhance diagnostic accuracy, personalise treatment plans, and predict patient outcomes.</em>

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Tyagi, Himanshu, Aditya Gupta, Saurabh Srivastava, Neeraj Kumari, Aryan Sharma, and Nikhil Sharma. "AI in Healthcare: Deep Learning Solutions for Lung Cancer Detection." International Journal of Research Publication and Reviews 6, sp5 (2025): 402–6. https://doi.org/10.55248/gengpi.6.sp525.1957.

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Pant, Sakshi. "Deep Learning for Personalized Healthcare Recommendations." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 470–75. http://dx.doi.org/10.22214/ijraset.2024.65093.

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Personalized healthcare refers to an evolving paradigm of providing appropriate medical treatments based on the particularities of the individual patient, where evidence-based management is enhanced with the use of technologies. Deep learning (DL) is placed within the umbrella of efficient systems known as artificial intelligence (AI), it assists in performing data processing with more accuracy, and making suggestions based on the unique health information of the health record e.g. EHRs, images and genetic data among others. This article gives an overview of deep learning techniques in develop

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Norgeot, Beau, Benjamin S. Glicksberg, and Atul J. Butte. "A call for deep-learning healthcare." Nature Medicine 25, no. 1 (2019): 14–15. http://dx.doi.org/10.1038/s41591-018-0320-3.

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Ganesh Viswanathan, Gaurav Samdani, Yawal Dixit, and Ranjith Gopalan. "Deep Learning." World Journal of Advanced Engineering Technology and Sciences 14, no. 3 (2025): 512–27. https://doi.org/10.30574/wjaets.2025.14.3.0149.

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Deep learning has revolutionized artificial intelligence by enabling machines to learn complex patterns from vast amounts of data. This white paper explores the fundamental principles of deep learning, including neural network architectures, training methodologies, and key advancements such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. We discuss applications across various domains, including computer vision, natural language processing, healthcare, and finance, highlighting real-world use cases and breakthroughs. Additionally, we examine th

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Mrs., P. Menaka, Subhashita J., Parthiban S., Sooraj S., and Dinesh R. "Medical Imaging Using Deep Learning." International Journal of Engineering and Management Research 14, no. 1 (2024): 40–43. https://doi.org/10.5281/zenodo.10646035.

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The healthcare sector has been transformed by deep learning, a kind of artificial intelligence Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two examples of deep learning techniques that have been used to evaluate medical pictures, forecast illness outcomes, and enhance patient care. This study examines the important strides made by deep learning in the fields of radiology, pathology, genomics, and electronic health records (EHRs). Additionally, it draws attention to the difficulties and moral issues that come with the application of deep learning in healthcare,

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Anjum, Uzma. "Artificial Intelligence, Machine Learning and Deep Learning In Healthcare." Bioscience Biotechnology Research Communications 14, no. 7 (2021): 144–48. http://dx.doi.org/10.21786/bbrc/14.7.36.

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Saxena, Ms Kavita, Rishabh Sharma, Rishav Kumar, and Roshan Kumar. "Disease Prediction Using Machine Learning and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2655–63. http://dx.doi.org/10.22214/ijraset.2022.42871.

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Abstract: Health being the state of complete physical and mental wellbeing is an imperative part of humankind .Healthcare sector been a capital incentive sector having complicated entry barrier for investors like acquiring land for making hospital, stamp duties on it, human resource crunch which further act as roadblock for the government in providing universal good healthcare services to its citizenry . In this regard artificial intelligence is leading to disruption in the healthcare sector which is helping poor in safeguarding them from been exploited by extravagant out of pocket expenditure

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Vinothkumar, Kolluru. "Healthcare Through AI: Integrating Deep Learning, Federated Learning, and XAI for Disease Management." International Journal of Soft Computing and Engineering (IJSCE) 13, no. 6 (2024): 21–27. https://doi.org/10.35940/ijsce.D3646.13060124.

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<strong>Abstract:</strong> The applications of Artificial Intelligence (AI) have been resonating across various fields for the past three decades, with the healthcare domain being a primary beneficiary of these innovations and advancements. Recently, AI techniques such as deep learning, machine learning, and federated learning have been frequently employed to address challenges in disease management. However, these techniques often face issues related to transparency, interpretability, and explainability. This is where explainable AI (XAI) plays a crucial role in ensuring the explainability of

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Dissertations / Theses on the topic "Deep Learning in Healthcare"

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AMODEO, MARIA. "Deep Learning Methods for Industry and Healthcare." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2963952.

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Torfi, Amirsina. "Privacy-Preserving Synthetic Medical Data Generation with Deep Learning." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99856.

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Deep learning models demonstrated good performance in various domains such as ComputerVision and Natural Language Processing. However, the utilization of data-driven methods in healthcare raises privacy concerns, which creates limitations for collaborative research. A remedy to this problem is to generate and employ synthetic data to address privacy concerns. Existing methods for artificial data generation suffer from different limitations, such as being bound to particular use cases. Furthermore, their generalizability to real-world problems is controversial regarding the uncertainties in def

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Mendes, David, M. J. Lopes, Artur Romão, and Irene Pimenta Rodrigues. "Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare." Bachelor's thesis, IGI Global, 2016. http://hdl.handle.net/10174/19286.

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The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for

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Beyki, Mohammad Reza. "Synthetic Electronic Medical Record Generation using Generative Adversarial Networks." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104642.

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It has been a while that computers have replaced our record books, and medical records are no exception. Electronic Health Records (EHR) are digital version of a patient's medical records. EHRs are available to authorized users, and they contain the medical records of the patient, which should help doctors understand a patient's condition quickly. In recent years, Deep Learning models have proved their value and have become state-of-the-art in computer vision, natural language processing, speech and other areas. The private nature of EHR data has prevented public access to EHR datasets. There

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Feghoul, Kevin. "Deep learning for simulation in healthcare : Application to affective computing and surgical data science." Electronic Thesis or Diss., Université de Lille (2022-....), 2024. http://www.theses.fr/2024ULILS033.

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Dans cette thèse, nous abordons diverses tâches dans les domaines de l’informatique affective et de la science des données chirurgicales qui ont le potentiel d’améliorer la simulation médicale. Plus précisément, nous nous concentrons sur quatre défis clés : la détection du stress, la reconnaissance des émotions, l’évaluation des compétences chirurgicales et la reconnaissance des gestes chirurgicaux. La simulation est devenue un élément important de la formation médicale, offrant aux étudiants la possibilité d’acquérir de l’expérience et de perfectionner leurs compétences dans un environnement

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Omar, Ali Nasra. "A Comparative study of cancer detection models using deep learning." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20468.

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Leukemi är en form av cancer som kan vara en dödlig sjukdom. För att rehabilitera och behandla sjukdomen krävs det en korrekt och tidig diagnostisering. För att minska väntetiden för testresultaten har de ordinära metoderna transformerats till automatiserade datorverktyg som kan analyser och diagnostisera symtom.I detta arbete, utfördes det en komparativ studie. Det man jämförde var två olika metoder som detekterar leukemia. Den ena metoden är en genetisk sekvenserings metod som är en binär klassificering och den andra metoden en bildbehandlings metod som är en fler-klassad klassificeringsmod

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Liu, Menghan. "PULMONARY FUNCTION MONITORING USING PORTABLE ULTRASONOGRAPHY AND PRIVACY-PRESERVING LEARNING." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1481034164747838.

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Zouambi, Meyssa. "Optimizing deep learning : navigating the field of neural architecture search from theory to practice." Electronic Thesis or Diss., Université de Lille (2022-....), 2023. http://www.theses.fr/2023ULILB054.

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Dans le domaine de l'apprentissage profond, la conception et l'optimisation des architectures neuronales sont essentielles pour obtenir des modèles performants. Ce processus, basé sur des essais et erreurs, a été effectué manuellement pendant des décennies et consomment beaucoup de temps et de ressources. Ce travail se penche sur le domaine de la recherche d'architecture neurale, ou Neural Architecture Search (NAS), une technique prometteuse qui vise à automatiser ce processus. Ce travail explore les complexités des NAS, mettant en évidence les défis à naviguer dans l'immense espace de recherc

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Nguyen, Trang Pham Ngoc. "A privacy preserving online learning framework for medical diagnosis applications." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2503.

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Electronic Health records are an important part of a digital healthcare system. Due to their significance, electronic health records have become a major target for hackers, and hospitals/clinics prefer to keep the records at local sites protected by adequate security measures. This introduces challenges in sharing health records. Sharing health records however, is critical in building an accurate online diagnosis framework. Most local sites have small data sets, and machine learning models developed locally based on small data sets, do not have knowledge about other data sets and learning mode

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Zhao, Yu [Verfasser], Bjoern H. [Akademischer Betreuer] Menze, Georg [Gutachter] Langs, and Bjoern H. [Gutachter] Menze. "Deep learning based medical image segmentation and classification for artificial intelligence healthcare / Yu Zhao ; Gutachter: Georg Langs, Bjoern H. Menze ; Betreuer: Bjoern H. Menze." München : Universitätsbibliothek der TU München, 2021. http://d-nb.info/1233428187/34.

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Books on the topic "Deep Learning in Healthcare"

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Chen, Yen-Wei, and Lakhmi C. Jain, eds. Deep Learning in Healthcare. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-32606-7.

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Murugeswari, K., B. Sundaravadivazhagan, S. Poonkuntran, and Thendral Puyalnithi. Deep Learning for Smart Healthcare. Auerbach Publications, 2024. http://dx.doi.org/10.1201/9781003469605.

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Kaur, Amandeep, Chetna Kaushal, Md Mehedi Hassan, and Si Thu Aung. Federated Deep Learning for Healthcare. CRC Press, 2024. http://dx.doi.org/10.1201/9781032694870.

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Xiao, Cao, and Jimeng Sun. Introduction to Deep Learning for Healthcare. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82184-5.

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Jain, Vishal, Jyotir Moy Chatterjee, Ishaani Priyadarshini, and Fadi Al-Turjman. Deep Learning for Healthcare Decision Making. River Publishers, 2023. http://dx.doi.org/10.1201/9781003373261.

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Singh, Krishna Kant, Akansha Singh, Jenn-Wei Lin, and Ahmed A. Elngar. Deep Learning and IoT in Healthcare Systems. Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003055082.

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Jena, Om Prakash, Bharat Bhushan, Nitin Rakesh, Parma Nand Astya, and Yousef Farhaoui. Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems. CRC Press, 2022. http://dx.doi.org/10.1201/9781003189053.

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Kumar, Ajay, Deepak Dembla, Seema Tinker, and Surbhi Bhatia Khan. Handbook of Deep Learning Models for Healthcare Data Processing. CRC Press, 2025. https://doi.org/10.1201/9781003467281.

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Jena, Om Prakash, Bharat Bhushan, and Utku Kose. Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications. CRC Press, 2022. http://dx.doi.org/10.1201/9781003226147.

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Patel, Govind Singh, Seema Nayak, and Sunil Kumar Chaudhary. Machine Learning, Deep Learning, Big Data, and Internet of Things for Healthcare. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003227595.

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Book chapters on the topic "Deep Learning in Healthcare"

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Selvi, M. Senthamil, K. Deepa, S. Jansi Rani, and N. Saranya. "Deep Learning: Healthcare." In Cyber-Physical Systems and Industry 4.0. Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003129790-14.

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Pavithra, V., and V. Jayalakshmi. "Deep Learning in Healthcare." In Intelligent Healthcare. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67051-1_11.

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Vij, Renu. "Revolutionizing Healthcare." In Federated Deep Learning for Healthcare. CRC Press, 2024. http://dx.doi.org/10.1201/9781032694870-2.

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Belhaouari, Samir Brahim, and Ashhadul Islam. "Deep Learning in Healthcare." In Multiple Perspectives on Artificial Intelligence in Healthcare. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67303-1_13.

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Kaul, Deeksha, Harika Raju, and B. K. Tripathy. "Deep Learning in Healthcare." In Studies in Big Data. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75855-4_6.

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Priya, L., A. Sathya, and S. ThangaRevathi. "Deep Learning in Healthcare." In Deep Learning and Edge Computing Solutions for High Performance Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60265-9_8.

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Deepa, C., and K. Tharageswari. "Deep Learning for Healthcare." In Deep Learning and IoT in Healthcare Systems. Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003055082-1.

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Trivizakis, Eleftherios, and Kostas Marias. "Deep Learning Fundamentals." In Imaging Informatics for Healthcare Professionals. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25928-9_6.

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Nandy, Aadrita, Jyoti Choudhary, Joanne Fredrick, T. S. Zacharia, Tom K. Joseph, and Veerpal Kaur. "Machine Learning for Healthcare." In Federated Deep Learning for Healthcare. CRC Press, 2024. http://dx.doi.org/10.1201/9781032694870-5.

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Singh, Bhupinder, and Christian Kaunert. "FedHealth in Wearable Healthcare, Orchestrated Federated Deep Learning for Smart Healthcare." In Federated Deep Learning for Healthcare. CRC Press, 2024. http://dx.doi.org/10.1201/9781032694870-16.

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Conference papers on the topic "Deep Learning in Healthcare"

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Thakur, Shaktisinh, and Supriya Narad. "Deep Learning Models in Healthcare." In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024. https://doi.org/10.1109/idicaiei61867.2024.10842703.

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G, Kothai, George princess T, Nandhagopal Subramani, Balakrishnan D, Ravi P, and S. Amutha. "Improving Healthcare with Machine Learning and Deep Learning." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763173.

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Gadkari, Ayush Pravin, Swapnil Gundewar, Mansvi Kishorrao Diagavhane, Prajyot Raju Yesankar, Sakshi Prakash Anpan, and Anushka Atul Arghode. "Deep Learning Techniques for Enhancing Image Recognition in Healthcare." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933095.

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Lanka, Surekha, and Taipida Moodhitaporn. "IoT Security Enhancements in Smart Healthcare Using Federated Learning." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933330.

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Jihane, Benbrik, Rattal Salma, Ghoumid Kamal, and Ar-Reyouchi El Miloud. "Advancing Healthcare Diagnostics with a Hybrid AI Model." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933484.

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Charan, Bakka, Dasu Jaswanth, E. Hemanth, and Mathireddy Sumanth Naidu. "Machine Learning and Deep Learning Approaches for Healthcare Predictive Analytics." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10689833.

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Nomula, Varun Kumar, Abdul Sajid Mohammed, Anuteja Reddy Neravetla, and S. Dhanasekaran. "Leveraging Deep Learning in Implementing Efficient Healthcare Processes." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726220.

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Bokde, Rohit, and Pragati Dongare. "Evolution of Wearable Healthcare Technology: Opportunities, Challenges and Applications." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933459.

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Ali B H, Baba Fakruddin, L. Guganathan, Prasanta Kumar Parida, D. Samundeeswari, and Sridhar S. "Optimized Stacked Sparse Autoencoder for IoT-Cloud Smart Healthcare Networks." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933460.

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Bhatti, Uzair Aslam, Yang Ke Yu, O. Zh Mamyrbayev, A. A. Aitkazina, Tang Hao, and N. O. Zhumazhan. "Recommendations for Healthcare: An Interpretable Approach Using Deep Learning." In 2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2024. https://doi.org/10.1109/prai62207.2024.10827288.

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Reports on the topic "Deep Learning in Healthcare"

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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes

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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k

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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject

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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-

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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap

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Yeates, Karen, and Taim Saeed. Consent or Compromise? The Hidden Costs of AI in Cervical Cancer Screening. Balsillie School of International Affairs, 2025. https://doi.org/10.51644/bcs011.

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Tanzania is one of nine global country sites collecting data from cervical cancer screening that is being used to further develop, train and validate a deep learning algorithm to be deployed as software on devices that take cervical images. In an era when AI holds a seemingly endless horizon of potential to revitalize underserved healthcare systems, is the race to develop it leading us to deprioritize the security of those systems and the rights of the patients within them? Karen Yeates, one of the authors of this case study and research scientist studying strategies to improve cervical cancer

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Catanach, Thomas, and Jed Duersch. Efficient Generalizable Deep Learning. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1760400.

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Dell, Melissa. Deep Learning for Economists. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w32768.

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Groh, Micah. NOvA Reconstruction using Deep Learning. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1462092.

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Geiss, Andrew, Joseph Hardin, Sam Silva, William Jr., Adam Varble, and Jiwen Fan. Deep Learning for Ensemble Forecasting. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769692.

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